Precipitation index estimation apparatus

ABSTRACT

A precipitation index estimation apparatus includes a data collection unit and an estimation processing unit. The data collection unit is configured to collect rainfall amount data that is detected by a rainfall amount sensor in one or more vehicles positioned in a predetermined area within a predetermined period. The estimation processing unit is configured to estimate a precipitation index indicating an intensity of precipitation in the predetermined area within the predetermined period, based on the collected rainfall amount data.

INCORPORATION BY REFERENCE

The disclosure of Japanese Patent Application No. 2018-184595 filed onSep. 28, 2018 including the specification, drawings and abstract isincorporated herein by reference in its entirety.

BACKGROUND 1. Technical Field

The present disclosure relates to a technology for estimating aprecipitation index indicating an intensity of precipitation based oninformation acquired in a vehicle.

2. Description of Related Art

Japanese Unexamined Patent Application Publication No. 2012-215969discloses a car navigation device that transmits, to a managementdevice, information indicating whether a windshield wiper of a vehicleis being operated or not, together with current time information orcurrent position information. By receiving the information indicatingwhether the windshield wiper of the vehicle is being operated or not,the management device sets the weather of an observation point at whicha photovoltaic power generating device is installed.

SUMMARY

Conventionally, a service of presenting, to users, an estimated value ofa current precipitation amount in each area has been provided.Particularly in recent years, sudden and localized heavy rains due tounstable atmospheric conditions often occur. There is therefore a needto estimate a current precipitation amount with high accuracy fordisaster prevention, or the like.

The present disclosure provides a technology for estimating aprecipitation index indicating an intensity of precipitation based oninformation acquired in a vehicle.

A precipitation index estimation apparatus according to an aspect of thedisclosure includes a data collection unit and an estimation processingunit. The data collection unit is configured to collect rainfall amountdata that is detected by a rainfall amount sensor in one or morevehicles positioned in a predetermined area within a predeterminedperiod. The estimation processing unit is configured to estimate aprecipitation index indicating an intensity of precipitation in thepredetermined area within the predetermined period, based on thecollected rainfall amount data.

With the above aspect, since the estimation processing unit estimatesthe precipitation index within the predetermined period based on therainfall amount data actually detected within the predetermined period,the accuracy of estimating the precipitation index can be increased.

The estimation processing unit may include an index derivation unitconfigured to derive a rainfall amount index representing the collectedrainfall amount data, and a precipitation index estimation unitconfigured to estimate the precipitation index from the rainfall amountindex.

The estimation processing unit may include an index derivation unitconfigured to determine an index representing rainfall amount data foreach vehicle, and derive, using the index, a rainfall amount indexrepresenting the rainfall amount data for the one or more vehicles, anda precipitation index estimation unit configured to estimate theprecipitation index from the rainfall amount index.

When the number of pieces of the rainfall amount data detected in avehicle, included in the one or more vehicles, within the predeterminedperiod is less than a predetermined value, the index derivation unitneed not determine the index representing the rainfall amount data forthe vehicle.

With the foregoing aspect of the present disclosure, a technology forestimating a precipitation index based on information acquired in avehicle can be provided.

BRIEF DESCRIPTION OF THE DRAWINGS

Features, advantages, and technical and industrial significance ofexemplary embodiments will be described below with reference to theaccompanying drawings, in which like numerals denote like elements, andwherein:

FIG. 1 is a diagram illustrating an overview of an informationprocessing system according to an embodiment;

FIG. 2A is a list illustrating an example of vehicle state information;

FIG. 2B is a list illustrating another example of the vehicle stateinformation;

FIG. 3 is a diagram illustrating unit areas for each of which aprecipitation index is estimated;

FIG. 4 is a diagram illustrating functional blocks of a precipitationindex estimation apparatus;

FIG. 5 is a table illustrating examples of collected operation modedata;

FIG. 6 is a table illustrating results of counting the number of piecesof operation mode data, for each kind of operation mode;

FIG. 7 illustrates a precipitation level correspondence table;

FIG. 8 illustrates a weight correspondence table;

FIG. 9 is a table illustrating an exemplified operation mode of eachvehicle;

FIG. 10 is a table illustrating vehicle speed at the time at which theoperation mode data is acquired;

FIG. 11 is a table illustrating results of counting the number of piecesof operation mode data, for each kind of operation mode;

FIG. 12 is a table illustrating examples of collected rainfall amountdata; and

FIG. 13 is a table illustrating an example of an index derived for eachvehicle.

DETAILED DESCRIPTION OF EMBODIMENTS

FIG. 1 illustrates an overview of an information processing system 1according to an embodiment. The information processing system 1 includesa server apparatus 3, a precipitation index estimation apparatus 10connected to the server apparatus 3, a weather information presentationapparatus 7 that presents weather information to users, a plurality ofwireless stations 4, and a plurality of vehicles 5. The server apparatus3, the weather information presentation apparatus 7, and the wirelessstations 4 may be connected to one another via a network 2, such as theInternet.

A control device 6 mounted on each vehicle 5 has a wirelesscommunication function, and is connected to the server apparatus 3 viathe wireless station 4, which is a base station. The number of vehicles5 is not limited to three. In the information processing system 1according to the present embodiment, it is assumed that a large numberof vehicles 5 generate vehicle state information and periodicallytransmit the vehicle state information to the server apparatus 3.

The server apparatus 3 is installed in a data center, and receives thevehicle state information transmitted from the control device 6 of eachvehicle 5. The vehicle state information includes traffic informationgenerated by an in-vehicle navigation device, and controller areanetwork (CAN) information generated by an electronic control unit (ECU)or various sensors provided in the vehicle 5, and transmitted on theCAN. The precipitation index estimation apparatus 10 collectsrain-related data included in the vehicle state information received bythe server apparatus 3, and executes a process of estimating aprecipitation index indicating an intensity of precipitation for eachpredetermined period. In the present embodiment, the process ofestimating a precipitation index includes a process of estimating anindex associated with a precipitation amount, and, for example, anestimated level of the precipitation may be derived. The serverapparatus 3 and the precipitation index estimation apparatus 10 may beintegrated with each other, and the server apparatus 3 may be equippedwith a precipitation index estimation function of the precipitationindex estimation apparatus 10.

The precipitation index estimation apparatus 10 estimates aprecipitation index in each of a plurality of areas, for example, everyfour minutes, and provides the estimated precipitation index data to theweather information presentation apparatus 7 via the server apparatus 3.The server apparatus 3 and the weather information presentationapparatus 7 may be connected to each other via a dedicated line. Basedon the precipitation index data transmitted from the precipitation indexestimation apparatus 10, rain cloud conditions acquired from rain cloudradars installed across the country, or the like, the weatherinformation presentation apparatus 7 generates an estimated value of acurrent precipitation amount in each of the plurality of areas, andpresents the estimated value of the current precipitation amount to theusers on a web page or the like. By taking into account theprecipitation index data that is estimated by the precipitation indexestimation apparatus 10 based on the vehicle state information, theweather information presentation apparatus 7 can acquire the estimatedvalue of the current precipitation amount with high accuracy.

FIGS. 2A and 2B illustrate examples of the vehicle state information.The vehicle state information includes the traffic information 8 and theCAN information 9. FIG. 2A illustrates items included in the trafficinformation 8. The traffic information 8 includes items such as avehicle identification number (VIN), an ID of a road link on which thevehicle 5 has travelled, a date and time of entry into the road link, adegree of congestion of the road on which the vehicle 5 has travelled,and an average vehicle speed. In the vehicle 5, the in-vehiclenavigation device generates the traffic information 8. The controldevice 6 transmits the traffic information 8 to the server apparatus 3at a predetermined first cycle. The first cycle may be several minutes.The transmitted traffic information 8 includes information on a roadlink that the vehicle 5 has passed after transmission of the lasttraffic information.

FIG. 2B illustrates items included in the CAN information 9. The CANinformation 9 includes items such as a VIN, a date and time, a latitudeand longitude, vehicle speed, acceleration, operation data, rain-relateddata, and a state of a seatbelt. The control device 6 acquires data oneach item and generates the CAN information 9. The control device 6transmits the CAN information 9 to the server apparatus 3 at apredetermined second cycle. The second cycle may be from several tens ofseconds to one minute.

The data sampling cycle may vary depending on the item. For example,data on vehicle speed and data on acceleration may each be acquired at acycle of several hundred milliseconds, and rain-related data may beacquired at a cycle of several tens of seconds. The data on each of theitems, such as the vehicle speed, the acceleration, the operation data,the rain-related data, and the state of the seatbelt, is associated withthe date and time at which the data is acquired and the latitude andlongitude at which the data is acquired.

The rain-related data included in the CAN information 9 is used toestimate a precipitation index indicating an intensity of precipitation,and is used in the process of estimating the precipitation index, whichis executed by the precipitation index estimation apparatus 10. In thepresent embodiment, the rain-related data includes operation mode dataindicating an operation mode of a windshield wiper and/or data on arainfall amount detected by a rainfall amount sensor.

The windshield wiper of each vehicle 5 is a device for wiping rain fromthe front windshield, or the like, and has a plurality of kinds ofoperation modes. The windshield wiper according to the presentembodiment has the following four kinds of operation modes, and theoperation mode data included in the CAN information 9 indicates anoperation mode selected from the four kinds of operation modes.

(1) Stop Mode

In stop mode, an operation switch of the windshield wiper is off. In thestop mode, the windshield wiper does not operate.

(2) Intermittent Mode

In intermittent mode, the windshield wiper operates at regularintervals. The intermittent mode is often selected when the rainfall islight.

(3) Low-Speed Mode

In low-speed mode, the windshield wiper operates continuously at lowspeed. The low-speed mode is often selected when the rainfall isslightly heavy, such as when an hourly rainfall amount is 10 mm or moreand less than 20 mm.

(4) High-Speed Mode

In high-speed mode, the windshield wiper operates continuously at highspeed. The high-speed mode is often selected when the rainfall is of ahigh intensity, such as when the hourly rainfall amount is 20 mm ormore.

The rainfall amount sensor is mounted on each vehicle 5 according to thepresent embodiment. The rainfall amount sensor (also referred to as arain sensor) is attached to, for example, an upper portion of the frontwindshield. The rainfall amount sensor may include an infrared lightemitting element, an infrared light receiving element, and amicrocomputer that executes processes of controlling the emission of thelight emitting element and detecting the rainfall amount. In therainfall amount sensor, infrared light emitted from the light emittingelement is reflected by the front windshield, and enters the lightreceiving element. However, when there are raindrops on the frontwindshield, some of the infrared light penetrates the windshield, andthus the amount of infrared light that enters the light receivingelement decreases. Therefore, when there are a lot of raindrops on thewindshield, the amount of light received by the light receiving elementis relatively small, whereas when there are a few raindrops on thewindshield, the amount of light received by light receiving element isrelatively large. In this manner, the amount of light received by thelight receiving element is correlated with the rainfall amount. Themicrocomputer has a function of detecting a rainfall amount (mm/h) fromthe amount of light received by light receiving element.

The rain-related data according to the present embodiment may includethe operation mode data indicating an operation mode of the windshieldwiper, may include data on a rainfall amount detected by the rainfallamount sensor, or may include both of them.

The precipitation index estimation apparatus 10 has a function ofacquiring the vehicle state information transmitted from each vehicle 5and estimating a precipitation index indicating an intensity ofprecipitation in a predetermined area within a predetermined period.FIG. 3 illustrates unit areas for each of which a precipitation indexwithin a predetermined period is estimated. The precipitation indexestimation apparatus 10 estimates a precipitation index within apredetermined period, for each of the unit areas defined by dividing amap with longitudinal lines and lateral lines. The map may be dividedinto a plurality of areas such that the areas have substantially thesame size. Alternatively, the area size may vary depending on, forexample, population density.

The map may be divided into a plurality of areas in any given method. Inthe example illustrated in FIG. 3, the map is divided into meshes ofsubstantially the same size, based on latitude and longitude. Forexample, in Japan, there is a standard area mesh, set based on latitudeand longitude, for use in statistics on each area. The precipitationindex estimation apparatus 10 may use this standard area mesh as a unitarea for estimation of a precipitation index. In addition, in thestandard area mesh, a primary mesh to a tertiary mesh with differentsizes are defined, and the length of one side of the tertiary mesh isabout 1 km. The precipitation index estimation apparatus 10 may set thetertiary mesh as a unit area for estimation of a precipitation index,but a smaller mesh or a larger mesh may also be set as a unit area forestimation of a precipitation index.

FIG. 4 illustrates functional blocks of the precipitation indexestimation apparatus 10. The precipitation index estimation apparatus 10includes a vehicle state information acquisition unit 20, a plurality ofdata collection units 30 a, 30 b, . . . , 30 z (hereinafter referred toas a “data collection unit 30”, unless otherwise specificallydistinguished), and a plurality of estimation processing units 40 a, 40b, . . . , 40 z (hereinafter referred to as an “estimation processingunit 40”, unless otherwise specifically distinguished). The datacollection unit 30 has a function of collecting rain-related data withina predetermined period. The estimation processing unit 40 has a functionof statistically processing the collected rain-related data, therebyestimating a precipitation index indicating an intensity ofprecipitation within the predetermined period.

The data collection unit 30 includes an operation mode data collectionunit 32 and a rainfall amount data collection unit 34. The estimationprocessing unit 40 includes a statistical processing unit 42, aprecipitation index estimation unit 48, a correction unit 50, a usedetermination unit 52, and a storage unit 54. The statistical processingunit 42 has a function of statistically processing the rain-relateddata, and includes a proportion derivation unit 44 and an indexderivation unit 46.

Each function of the precipitation index estimation apparatus 10 may beimplemented by a large-scale integration (LSI) including a circuitblock, a memory, and other elements in terms of hardware, and may beimplemented by system software, an application program, or the like,loaded in the memory, in terms of software. Therefore, a person skilledin the art would understand that each function of the precipitationindex estimation apparatus 10 may be implemented in various forms, byhardware only, by software only, or by a combination of hardware andsoftware, and is not limited to any one thereof.

The vehicle state information acquisition unit 20 acquires all thevehicle state information received by the server apparatus 3. When theserver apparatus 3 receives the vehicle state information from thevehicle 5 and stores it in a designated storage device, the vehiclestate information acquisition unit 20 may immediately read and therebyacquire the vehicle state information from the storage device. When theprecipitation index estimation apparatus 10 is provided as one functionof the server apparatus 3, the vehicle state information acquisitionunit 20 may receive the vehicle state information from a receiving unitof the server apparatus 3.

A combination of one data collection unit 30 and one estimationprocessing unit 40 is allocated to one unit area. For example, acombination of the data collection unit 30 a and the estimationprocessing unit 40 a is assigned to estimation of a precipitation indexof a first area, and a combination of the data collection unit 30 b andthe estimation processing unit 40 b is assigned to estimation of aprecipitation index of a second area. Therefore, the number ofcombinations of the data collection unit 30 and the estimationprocessing unit 40 may be equal to the number of areas that are definedby dividing the map.

The data collection unit 30 collects rain-related data for estimating aprecipitation index within a predetermined period, from the vehiclestate information generated in one or more vehicles. Specifically, thedata collection unit 30 collects the rain-related data acquired in anarea to which it is allocated, from the CAN information 9 that isacquired by the vehicle state information acquisition unit 20. Asillustrated in FIG. 2B, the rain-related data is associated with thedate and time at which the rain-related data is acquired and thelatitude and longitude at which the rain-related data is acquired, andthe data collection unit 30 collects the rain-related data associatedwith the latitude and longitude of the area to which the data collectionunit 30 is allocated.

First Embodiment

In a first embodiment, the operation mode data collection unit 32collects the operation mode data indicating an operation mode of thewindshield wiper, which is acquired in one or more vehicles positionedin a predetermined area within a predetermined period. Based on aproportion of each of a plurality of kinds of operation modes, which isderived from the collected operation mode data, the precipitation indexestimation unit 48 estimates a precipitation index indicating anintensity of precipitation in the predetermined area within thepredetermined period. By using the proportion of each kind of operationmode, which is acquired by statistical processing, the precipitationindex estimation unit 48 can estimate the precipitation index within thepredetermined period with high accuracy.

FIG. 5 illustrates an example of the operation mode data collected bythe operation mode data collection unit 32. The operation mode datacollection unit 32 collects, from the vehicle state information acquiredby the vehicle state information acquisition unit 20, the operation modedata acquired in one or more vehicles positioned in an area having anarea ID, “XXXXXX”, within a period from 15:00:00 to 15:03:59.

In the example illustrated in FIG. 5, there are five vehicles 5—vehiclesA, B, C, D, and E—that travel in the area having the area ID, “XXXXXX”,within the period from 15:00:00 to 15:03:59. Among the five vehicles 5,vehicles A, B, C, and D travel in the area for four minutes from15:00:00 to 15:03:59, and vehicle E travels in the area from 15:03:00.

The proportion derivation unit 44 counts the number of pieces of theoperation mode data, for each of the plurality of kinds of operationmodes indicated in the collected operation mode data, and derives aproportion of each of the plurality of kinds of operation modes. FIG. 6illustrates the results of counting the number of pieces of theoperation mode data acquired in the vehicles, for each of the pluralityof kinds of operation modes. For example, in vehicle A, none of theoperation mode data indicating the stop mode, the operation mode dataindicating the intermittent mode, and the operation mode data indicatingthe low-speed mode are acquired, and 12 pieces of the operation modedata indicating the high-speed mode are acquired, that is, the operationmode data indicating the high-speed mode is acquired 12 times, duringthe period of four minutes.

The proportion derivation unit 44 counts, for each of the plurality ofkinds of operation modes, the number of times that the operation modedata is acquired in all the vehicles, as follows:

Stop mode: 0 time

Intermittent mode: 3 times

Low-speed mode: 9 times

High-speed mode: 39 times

Based on the above counting results, the proportion derivation unit 44derives a proportion of each of the plurality of kinds of operationmodes, as follows:

Stop mode: 0.000

Intermittent mode: 0.059

Low-speed mode: 0.176

High-speed mode: 0.765

Based on the proportion of each of the plurality of kinds of operationmodes, the precipitation index estimation unit 48 estimates aprecipitation index in the area to which it is allocated, within thepredetermined period.

FIG. 7 illustrates a precipitation level correspondence table stored inthe storage unit 54. In the precipitation level correspondence table, anestimated precipitation level is defined for each operation mode. Theprecipitation index estimation unit 48 may read an estimatedprecipitation level corresponding to an operation mode with the highestproportion from the storage unit 54, and derive the estimatedprecipitation level as a precipitation index. In this example, since theproportion of the high-speed mode is the highest, the precipitationindex estimation unit 48 reads the estimated precipitation level “heavy”from the storage unit 54, and thus determines that the precipitationlevel in an allocated area is “heavy”.

Each estimation processing unit 40 estimates a precipitation index (inthis example, a precipitation level) in the allocated area, from theoperation mode data during the four minutes from 15:00:00 to 15:03:59.The precipitation index estimation apparatus 10 supplies, to the serverapparatus 3, the precipitation index data for all the areas within thepredetermined period, and the server apparatus 3 transmits theprecipitation index data for all the areas to the weather informationpresentation apparatus 7. In the present embodiment, the precipitationindex estimation apparatus 10 provides the precipitation index data forall the areas to the weather information presentation apparatus 7 everyfour minutes, so that the weather information presentation apparatus 7can update real time weather information with high accuracy every fourminutes, and present it to the users.

The precipitation index estimation unit 48 may estimate a precipitationindex based on a result of another statistical processing executed bythe statistical processing unit 42. With this estimation method, theprecipitation index estimation unit 48 uses a windshield wiper operationmode index derived by the statistical processing, thereby increasing theaccuracy of estimating the precipitation index.

Specifically, the index derivation unit 46 of the statistical processingunit 42 derives an operation mode index indicating a stage of theoperation mode of the windshield wiper, based on the proportion of eachof the plurality of kinds of operation modes. A weight for calculatingthe index is defined for each operation mode. The index derivation unit46 may calculate an operation mode index indicating a stage of theoperation mode, by multiplying the proportion of each operation mode bythe weight for the operation mode, and adding up the values acquiredthrough multiplication performed for all the operation modes.

FIG. 8 illustrates a weight correspondence table stored in the storageunit 54. In the weight correspondence table, a weight for calculating anindex is defined for each operation mode. The index derivation unit 46refers to the weight correspondence table and calculates, using thefollowing calculation formula, an index (an operation mode index)indicating a stage of the operation mode. The minimum value of theoperation mode index is 0, and the maximum value thereof is 10.

(Operation mode index)=Σ(proportion of each operation mode)×(weight ofthe operation mode)

An operation mode index is calculated using the proportion of each ofthe plurality of kinds of operation modes illustrated in FIG. 6, asfollows.

(Operation mode index)=0.000×0+0.059×2+0.176×6+0.765×10=8.824

The operation mode index is used to estimate a precipitation index. Thecalculated index may be converted to an integer value by rounding offthe decimal places.

When all the windshield wipers are operated in the high-speed mode, theoperation mode index is the maximum value of 10. The calculatedoperation mode index expresses a degree of the operation state of thewindshield wiper with respect to the maximum value, and is highlycorrelated to the precipitation amount. The precipitation indexestimation unit 48 estimates a precipitation index in the allocated areawithin the predetermined period from the operation mode index derived bythe index derivation unit 46. For example, when the weather informationpresentation apparatus 7 expresses the precipitation amount on a scaleof level 1 to level 10 and presents it to the users, the precipitationindex estimation unit 48 may derive, as precipitation index data, alevel of the precipitation amount that corresponds to the operation modeindex. The storage unit 54 may store a correspondence table illustratinga correspondence between the operation mode index and the level of theprecipitation amount, and the precipitation index estimation unit 48 mayderive the level of the precipitation amount to be used as theprecipitation index, by referring to the correspondence table. Inaddition, the precipitation index estimation unit 48 may acquire thelevel of the precipitation amount by correcting the operation mode indexwith a predetermined correction function.

According to the above method, the proportion derivation unit 44 derivesthe proportion of each of the plurality of kinds of operation modes fromall the operation mode data for the windshield wiper acquired within thepredetermined period. With this statistical processing method, atendency for selecting a windshield wiper operation mode within thepredetermined period is derived.

Here, it is known that the operation mode of the windshield wiper tendsto reflect a preference of a driver. For example, while some driversselect the high-speed mode in case of light rain, others select thelow-speed mode in case of heavy rain. In the example illustrated in FIG.5, although the high-speed mode is selected and maintained in vehicles Aand D throughout the four minutes, the possibility that the driversprefer the high-speed mode regardless of the intensity of the raincannot be denied.

When a large number of pieces of operation mode data reflecting thedriver's preference are acquired, estimation of a precipitation index ishighly influenced by the driver's preference. Therefore, in order toreduce the influence to be exerted on estimation of a precipitationindex by the number of pieces of acquired data, which varies among thevehicles 5, the following process may be executed. The proportionderivation unit 44 may determine one kind of operation mode for eachvehicle. Then, for each kind of operation mode, the proportionderivation unit 44 counts the number of vehicles 5 for which the kind ofoperation mode is determined. The proportion derivation unit 44 thenderives a proportion of each of the plurality of kinds of operationmodes.

FIG. 9 illustrates an example of an operation mode determined for eachvehicle. For each vehicle, the proportion derivation unit 44 maydetermine the operation mode indicated in the operation mode data whichis acquired most frequently, as the operation mode of the vehicle. Inthe example illustrated in FIG. 9, the proportion derivation unit 44determines the operation mode of each vehicle as follows:

Vehicle A: high-speed mode

Vehicle B: high-speed mode

Vehicle C: low-speed mode

Vehicle D: high-speed mode

Vehicle E: high-speed mode

For each kind of operation mode, the proportion derivation unit 44counts the number of vehicles for which the kind of operation mode isdetermined. The results of the counting are as follows:

Stop mode: 0

Intermittent mode: 0

Low-speed mode: 1

High-speed mode: 4

Based on the above counting results, the proportion derivation unit 44derives a proportion of each of the plurality of kinds of operationmodes as follows:

Stop mode: 0.000

Intermittent mode: 0.000

Low-speed mode: 0.200

High-speed mode: 0.800

Based on the proportion of each operation mode, the precipitation indexestimation unit 48 estimates a precipitation index in the allocated areawithin the predetermined period. The precipitation index estimation unit48 may read, from the storage unit 54, an estimated precipitation levelfor the operation mode with the highest proportion (see FIG. 7), andestimate a precipitation index. In this example, since the proportion ofthe high-speed mode is the highest, the precipitation index estimationunit 48 reads the estimated precipitation level “heavy” from the storageunit 54, and determines that the precipitation level in the allocatedarea is “heavy”.

The precipitation index estimation unit 48 may estimate a precipitationindex in the allocated area within the predetermined period, using anoperation mode index derived by the index derivation unit 46. The indexderivation unit 46 calculates the operation mode index according to thefollowing calculation formula:

(Operation mode index)=(proportion of each operation mode)×(weight ofthe operation mode).

That is, the index derivation unit 46 calculates the operation modeindex as follows:

(Operation mode index)=0.000×0+0.000×2+0.200×6+0.800×10=8.4

The precipitation index estimation unit 48 estimates the precipitationindex in the allocated area within the predetermined period from theoperation mode index derived by the index derivation unit 46. Asdescribed above, the precipitation index estimation unit 48 may derive alevel of the precipitation amount corresponding to the operation modeindex, as the precipitation index data. The storage unit 54 may store acorrespondence table illustrating a correspondence between the operationmode index and the precipitation level, and the precipitation indexestimation unit 48 may derive the level of the precipitation amount byreferring to the correspondence table. In addition, the precipitationindex estimation unit 48 may acquire the level of the precipitationamount by correcting the operation mode index with a predeterminedcorrection function.

In the above-described example in which the proportion derivation unit44 determines one operation mode for each vehicle, when the number ofpieces of operation data acquired in a vehicle within the predeterminedperiod is less than a predetermined value, the proportion derivationunit 44 need not determine the operation mode for the vehicle. Forexample, in the example illustrated in FIG. 9, although 12 pieces ofoperation mode data are acquired in each of vehicles A to D, only threepieces of operation mode data are acquired in vehicle E. When theminimum number of samples necessary for determining the operation modefor a vehicle is 5, the proportion derivation unit 44 need not determinethe operation mode for vehicle E in which less than 5 pieces ofoperation mode data are acquired. In this case, the proportionderivation unit 44 derives a proportion of each of the plurality ofkinds of operation modes based on the operation modes of vehicles A toD.

Note that, it is known that the amount of raindrops hitting the frontwindshield varies depending on the vehicle speed. As the vehicle speedincreases, the amount of raindrops hitting the front windshieldincreases, whereas as the vehicle speed decreases, the amount ofraindrops hitting the front windshield decreases. Therefore, while thevehicle is travelling on a highway, the amount of raindrops hitting thefront windshield increases even though rain is not so heavy, andaccordingly the driver tends to operate the windshield wiper in thehigh-speed mode while the vehicle is travelling at high speed.

Therefore, even though the operating mode data for the windshield wiperindicates the high-speed mode, there are cases in which an actualprecipitation amount is not large, depending on the vehicle speed at thetime of acquiring the operation mode data. Therefore, before thestatistical processing by the statistical processing unit 42, thecorrection unit 50 may correct the value of an operation mode data to beused for statistical processing, according to the vehicle speed at thetime at which the operation mode data is acquired.

FIG. 10 illustrates the vehicle speed at the time of acquiring theoperation mode data. The correction unit 50 corrects the value of anoperation mode data such that as the vehicle speed at the time ofacquiring the operation mode data is higher, the precipitation indexestimated based on the operation mode data is smaller. The correctionunit 50 may set only the operation mode data acquired when the vehicleis travelling at high speed, as the operation mode data to be corrected.Whether or not the vehicle is travelling at high speed may be determinedbased on whether the vehicle speed is equal to or higher than apredetermined vehicle speed (for example, 80 km/h).

In the example illustrated in FIG. 10, the correction unit 50 determinesthat vehicle E is travelling at high speed, and specifies the values ofthe three pieces of operation mode data for vehicle E as the operationmode data to be corrected. The correction unit 50 corrects the value ofthe operation mode data acquired when the vehicle is travelling at highspeed, as below.

In the example described above, the proportion derivation unit 44 countsthe number of pieces of the operation mode data acquired in all thevehicles, for each of the plurality of kinds of operation modes, andexecutes statistical processing for deriving the proportion of each kindof operation mode. In this statistical processing, the number of piecesof the operation mode data each indicating one kind of operation modeare counted, and thus one piece of the operation mode data is counted asone piece of operation mode data indicating one kind of operation modein the counting process.

Before counting the number of pieces of the operation mode data, foreach kind of operation mode, the correction unit 50 corrects one pieceof the operation mode data acquired when the vehicle is travelling athigh speed, to s (s<1) pieces. For example, when s is set to 0.5(s=0.5), the proportion derivation unit 44 counts three pieces of thehigh-speed mode data acquired in vehicle E (in other words, thehigh-speed mode data is acquired three times in vehicle E) as 1.5 (=3×s)pieces.

FIG. 11 illustrates results of counting the number of pieces of theoperation mode data acquired in the vehicles, for each of the pluralityof kinds of operation modes. The correction unit 50 corrects one pieceof the high-speed mode data acquired in vehicle E, to 0.5 pieces. Usingthe operation mode data corrected by the correction unit 50, theproportion derivation unit 44 counts, for each of the plurality of kindsof operation modes, the number of times that the operation mode data isacquired in all the vehicles, as follows:

Stop mode: 0 time

Intermittent mode: 3 times

Low-speed mode: 9 times

High-speed mode: 37.5 times

The correction unit 50 corrects one piece of the operation mode dataacquired in vehicle E to 0.5 pieces. As a result of the correction, thecounted value of the high-speed mode data has changed from that in thecounting results illustrated in FIG. 6. Based on the counting resultsillustrated in FIG. 11, the proportion derivation unit 44 derives aproportion of each of the plurality of kinds of operation modes, asfollows:

Stop mode: 0.000

Intermittent mode: 0.060

Low-speed mode: 0.182

High-speed mode: 0.758

The precipitation index estimation unit 48 estimates a precipitationindex in the allocated area within the predetermined period based on theproportion of each operation mode.

Based on the statistical processing results illustrated in FIG. 11, theindex derivation unit 46 derives an operation mode index, as follows:

(Operation mode index)=0.000×0+0.060×2+0.182×6+0.758×10=8.792

The operation mode index calculated based on the statistical processingresults illustrated in FIG. 6 is 8.824. It can be seen that theoperation mode index calculated based on the statistical processingresults illustrated in FIG. 11 is lower than that calculated based onthe statistical processing results illustrated in FIG. 6. In such amanner, when the vehicle speed at the time of acquiring the operationmode data is high, the correction unit 50 may correct the value used forthe statistical processing of the operation mode data, so that theoperation mode index derived by the statistical processing unit 42 issmaller than the operation mode index before the correction. As such,the statistical processing unit 42 can derive statistical processingresults with high accuracy.

There are some exceptional operation states of the windshield wiper whenit is raining. For example, when the vehicle 5 is travelling in atunnel, the driver does not operate the windshield wiper. Therefore, theoperation mode data acquired while the vehicle is travelling in a tunnelindicates the stop mode. However, this operation mode does not reflectthe weather condition. Therefore, the use determination unit 52determines whether to use the operation mode data for the statisticalprocessing, based on the road on which the vehicle is travelling at thetime of acquiring the operation mode data.

By using the road link ID included in the traffic information 8 and thelatitude and longitude included in the CAN information 9, the usedetermination unit 52 determines whether the vehicle 5 is travelling ona road on which the operation of the windshield wiper is not necessary(a windshield wiper-free road) at the time of acquiring the operationmode data. The windshield wiper-free road is typically a tunnel, butexamples of the windshield wiper-free road also include a road locatedbelow an elevated road. The use determination unit 52 may determinewhether the vehicle 5 is travelling on a windshield wiper-free road atthe time of acquiring the operation mode data, by specifying a road linkfrom the road link ID and the position of the vehicle 5 on the road linkfrom the latitude and longitude, and referring to a map database.Further, in the map database, attribute data indicating a road kind,such as a tunnel or the like, is associated with a position on the roadlink.

When the vehicle 5 is travelling on the windshield wiper-free road, theuse determination unit 52 determines to exclude the operation mode datafrom the statistical processing. On the other hand, when the vehicle 5is travelling on a road that is not a windshield wiper-free road, theuse determination unit 52 determines to include the operation mode dataacquired in the corresponding vehicle 5 in the statistical processing,that is, to use the operation mode data for the statistical processing.As such, the use determination unit 52 determines whether the operationmode data is to be used, so that the statistical processing unit 42 canappropriately execute the statistical processing using the operationmode data reflecting weather conditions.

Second Embodiment

In a second embodiment, the rainfall amount data collection unit 34collects rainfall amount data that is detected by the rainfall amountsensor, in one or more vehicles positioned in a predetermined areawithin a predetermined period. Based on the collected rainfall amountdata, the precipitation index estimation unit 48 estimates aprecipitation index indicating an intensity of precipitation in thepredetermined area within the predetermined period.

FIG. 12 illustrates an example of the rainfall amount data collected bythe rainfall amount data collection unit 34. In FIG. 12, a value of therainfall amount data shown at each time indicates the rainfall amount(mm/h) detected by the rainfall amount sensor. The rainfall amount datacollection unit 34 collects, from the vehicle state information acquiredby the vehicle state information acquisition unit 20, the rainfallamount data acquired in one or more vehicles positioned in an areahaving an area ID, “XXXXXX”, within a period from 15:00:00 to 15:03:59.

In the example illustrated in FIG. 12, there are five vehicles5—vehicles A, B, C, D, and E—that travel in the area having the area ID,“XXXXXX”, within the period from 15:00:00 to 15:03:59. Among the fivevehicles 5, vehicles A, B, C, and D travel in the area for four minutesfrom 15:00:00 to 15:03:59, and vehicle E travels in the area from15:03:00.

The index derivation unit 46 derives a rainfall amount indexrepresenting the collected rainfall amount data. The index derivationunit 46 may derive an average value of the rainfall amount within thepredetermined period, as a rainfall amount index. In the exampleillustrated in FIG. 12, the average value of the rainfall amount iscalculated to be 19.3 mm/h.

The precipitation index estimation unit 48 estimates a precipitationindex in the allocated area within the predetermined period, from therainfall amount index derived by the index derivation unit 46. Theprecipitation index estimation unit 48 may correct the average value ofthe rainfall amount with a predetermined correction function, therebyestimating a precipitation index.

As described in the first embodiment, the amount of raindrops hittingthe front windshield varies depending on the vehicle speed. For thisreason, there is a possibility that, while the vehicle is travelling,the rainfall amount sensor may detect a rainfall amount (mm/h) that islarger than the actual precipitation amount. Therefore, theprecipitation index estimation unit 48 may estimate a precipitationindex within a predetermined period by taking into account the vehiclespeed at the time of acquiring the rainfall amount data. For example,the precipitation index estimation unit 48 may calculate an averagevalue Vave of the vehicle speed at the time of acquiring the rainfallamount data and estimate a precipitation index by multiplying theaverage value of the rainfall amount (19.3 mm/h) by a correctioncoefficient α (α<1) acquired from the average value Vave of the vehiclespeed. The higher the average value Vave of the vehicle speed is, thesmaller the correction coefficient a is calculated. As such, in thesecond embodiment, the index derivation unit 46 derives a rainfallamount index representing the collected rainfall amount data, and theprecipitation index estimation unit 48 estimates a precipitation indexfrom the rainfall amount index. In the second embodiment, by using avalue detected by the rainfall amount sensor, the index derivation unit46 can estimate a precipitation index within a predetermined period withhigh accuracy.

Furthermore, the index derivation unit 46 may derive a median value ormode value of the rainfall amount within the predetermined period, as arainfall amount index.

In the above method, the index derivation unit 46 derives, from all therainfall amount data acquired within a predetermined period, an indexrepresenting the rainfall amount data. Through this statisticalprocessing method, a tendency of the rainfall amount data within thepredetermined period is derived.

There is a possibility that the rainfall amount sensor mounted on eachvehicle 5 may differ in terms of detection characteristics, depending onthe sensor sensitivity or the manner in which the rainfall amount sensoris mounted on the vehicle 5. Therefore, when a large number of pieces ofrainfall amount data detected by a rainfall amount sensor that does nothave appropriate detection characteristics are acquired, estimation of aprecipitation index is highly influenced by the values detected by thisrainfall amount sensor. Therefore, in order to reduce the influence tobe exerted on estimation of a precipitation index by the number ofpieces of acquired data, which varies among the vehicles 5, thefollowing process may be executed. The index derivation unit 46 mayfirst determine an index representing the rainfall amount data for eachvehicle, and then use the index for each vehicle to derive an indexrepresenting the rainfall amount data for the plurality of vehicles.

FIG. 13 illustrates an example of an index derived for each vehicle. Theindex derivation unit 46 derives an average value of the rainfall amountwithin the predetermined period as an index for each vehicle. In theexample illustrated in FIG. 13, the index derivation unit 46 determinesthe index of each vehicle, as follows:

Vehicle A: 17.8 (mm/h)

Vehicle B: 17.8 (mm/h)

Vehicle C: 17.7 (mm/h)

Vehicle D: 17.6 (mm/h)

Vehicle E: 45 (mm/h)

Using the index of each vehicle, the index derivation unit 46 derives arainfall amount index representing the rainfall amount data for theplurality of vehicles. When an average value of the indices of all thevehicles is used as an index, the index representing the rainfall amountdata for all the vehicles is 23.2 (mm/h). The index derivation unit 46may derive a median value or mode value of the rainfall amount withinthe predetermined period as an index.

The precipitation index estimation unit 48 estimates a precipitationindex in the allocated area within the predetermined period, from therainfall amount index derived by the index derivation unit 46. Theprecipitation index estimation unit 48 may correct the average value ofthe rainfall amount with a predetermined correction function, therebyestimating a precipitation index.

In the above-described example in which the index derivation unit 46determines an index representing the rainfall amount data for eachvehicle, when the number of pieces of rainfall amount data acquired in avehicle within the predetermined period is less than a predeterminedvalue, the index derivation unit 46 need not determine the indexrepresenting the rainfall amount data for the vehicle. For example, inthe example illustrated in FIG. 12, although 12 pieces of rainfallamount data are acquired in each of vehicles A to D, only three piecesof rainfall amount data are acquired in vehicle E. When the minimumnumber of samples necessary for determining the index representing therainfall amount data for a vehicle is 5, the index derivation unit 46need not determine an index for vehicle E in which less than 5 pieces ofrainfall amount data are acquired. In this case, the index derivationunit 46 derives a rainfall amount index representing the rainfall amountdata within the predetermined period based on the indices for vehicles Ato D.

In the second embodiment, in a similar manner to the first embodiment,before the statistical processing by the statistical processing unit 42,the correction unit 50 may correct the value of each piece of therainfall amount data according to the vehicle speed at the time at whichthe rainfall amount data is acquired. The correction unit 50 correctsthe value of the rainfall amount data such that as the vehicle speed atthe time of acquiring the rainfall amount data is higher, theprecipitation index estimated based on the rainfall amount data issmaller. The correction unit 50 may set only the rainfall amount dataacquired while the vehicle is travelling at high speed, as the rainfallamount data to be corrected.

In the example illustrated in FIG. 12, the correction unit 50 determinesthat vehicle E is travelling at high speed, and specifies the values ofthe three pieces of rainfall amount data for vehicle E as the rainfallamount data to be corrected. The correction unit 50 may correct thevalue of the rainfall amount data by multiplying the value of therainfall amount data by a correction coefficient β(β≤1) acquired fromthe vehicle speed at the time of acquiring the rainfall amount data. Thehigher the vehicle speed is, the smaller the correction coefficient β iscalculated.

In addition, in the second embodiment, the correction unit 50 mayspecify all the rainfall amount data as the rainfall amount data to becorrected, and correct the rainfall amount data by multiplying the valueof all the rainfall amount data by the correction coefficient β. Whenthe vehicle speed is zero, the correction coefficient 13 may be one(β=1). Since the value detected by the in-vehicle rainfall amount sensorincludes an error corresponding to the vehicle speed, the correctionunit 50 may remove this error from the rainfall amount data, and thusthe precipitation index estimation unit 48 can estimate a precipitationindex with high accuracy.

In the similar manner to the first embodiment, the use determinationunit 52 may determine whether to use the rainfall amount data for thestatistical processing, based on the road on which the vehicle 5 istravelling at the time of acquiring the rainfall amount data. By usingthe road link ID included in the traffic information 8 and the latitudeand longitude included in the CAN information 9, the use determinationunit 52 determines whether the vehicle 5 is travelling on a road onwhich the operation of the windshield wiper is not necessary (awindshield wiper-free road) at the time of acquiring the rainfall amountdata. The use determination unit 52 may determine whether the vehicle 5is travelling on a windshield wiper-free road at the time of acquiringthe rainfall amount data, by specifying a road link from the road linkID and the position of the vehicle 5 on the road link from the latitudeand longitude

When the vehicle 5 is travelling on the windshield wiper-free road, theuse determination unit 52 determines to exclude the rainfall amount datafrom the statistical processing. On the other hand, when the vehicle 5is travelling on a road that is not a windshield wiper-free road, theuse determination unit 52 determines to include the rainfall amount dataacquired in the corresponding vehicle 5 in the statistical processing,that is, to use the rainfall amount data for the statistical processing.As such, the use determination unit 52 determines whether the rainfallamount data is to be used, so that the statistical processing unit 42can appropriately execute the statistical processing using the rainfallamount data reflecting weather conditions.

The present disclosure has been described based on the embodiments and aplurality of examples. The present disclosure is not limited to theabove-described embodiments and examples, and variations such as designmodifications, and the like, can be made based on the knowledge of thoseskilled in the art.

In the first embodiment, the estimation processing unit 40 estimates aprecipitation index based on the operation mode data for the windshieldwiper. In the second embodiment, the estimation processing unit 40estimates a precipitation index based on the rainfall amount datadetected by the rainfall amount sensor. In a modified example, theestimation processing unit 40 may estimate a precipitation index basedon the operation mode data for the windshield wiper and the rainfallamount data.

Although all the vehicles 5 are provided with windshield wipers, it isassumed that not all the vehicles 5 are provided with rainfall amountsensors. Therefore, in the information processing system 1, there may bea vehicle 5 capable of transmitting both the operation mode data for thewindshield wiper and the rainfall amount data to the server apparatus 3,and a vehicle 5 capable of transmitting only the operation mode data forthe windshield wiper to the server apparatus 3. When the vehicle 5 cantransmit both the operation mode data and the rainfall amount data, theestimation processing unit 40 may estimate a precipitation index, basedon the rainfall amount data. The estimation processing unit 40 maygenerate first precipitation index data that is estimated based on therainfall amount data and second precipitation index data that isestimated based on the operation mode data within the same period. Bytaking into account the first precipitation index data and the secondprecipitation index data, the estimation processing unit 40 may generatethe precipitation index data to be provided to the weather informationpresentation apparatus 7.

What is claimed is:
 1. A precipitation index estimation apparatus,comprising: a data collection unit configured to collect rainfall amountdata that is detected by a rainfall amount sensor in one or morevehicles positioned in a predetermined area within a predeterminedperiod; and an estimation processing unit configured to estimate aprecipitation index indicating an intensity of precipitation in thepredetermined area within the predetermined period, based on thecollected rainfall amount data.
 2. The precipitation index estimationapparatus according to claim 1, wherein the estimation processing unitincludes an index derivation unit configured to derive a rainfall amountindex representing the collected rainfall amount data, and aprecipitation index estimation unit configured to estimate theprecipitation index from the rainfall amount index.
 3. The precipitationindex estimation apparatus according to claim 1, wherein the estimationprocessing unit includes an index derivation unit configured todetermine an index representing rainfall amount data for each of the oneor more vehicles and derive, using the index, a rainfall amount indexrepresenting the rainfall amount data for the one or more vehicles, anda precipitation index estimation unit configured to estimate theprecipitation index from the rainfall amount index.
 4. The precipitationindex estimation apparatus according to claim 3, wherein when the numberof pieces of the rainfall amount data detected in a vehicle, included inthe one or more vehicles, within the predetermined period is less than apredetermined value, the index derivation unit does not determine theindex representing the rainfall amount data for the vehicle.